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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Progress on Associate-Particle Imaging Algorithms, 2020

The present work describes progress on the development of imaging algorithms that use fast neutron signatures acquired using the associated-particle imaging (API) method. The present work complements ongoing work to develop neutron source and detector hardware to enable field inspection by investigating algorithms that are capable of discriminating between critical materials or extracting three-dimensional geometrical information from single-sided or transmission measurements. The present work is divided into three approaches:(1)Iterative reconstruction of inelastic gamma-ray emissions to perform three-dimensional time-of-flight imaging in a single view in either transmission or backscatter configurations. Iterative reconstruction enables image resolution better than the inherent TOF resolution.(2)Decomposition of registered neutron and x ray radiographs into an assumed material list for each pixel in the image.(3)Material identification using full spectral analysis that includes the emergent neutron and gamma ray energies, times, and angles.Progress for each approach is summarized for fiscal year 2020.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

MemXCT: Design, Optimization, Scaling, and Reproducibility of X-Ray Tomography Imaging

Here, this work extends our previous research entitled "MemXCT: Memory-centric X-ray CT Reconstruction with Massive Parallelization" that was originally published at SC19 conference (Hidayetoglu et al., 2019) with reproducibility of the computational imaging performance. X-ray computed tomography (XCT) is regularly used at synchrotron light sources to study the internal morphology of materials at high resolution. However, experimental constraints, such as radiation sensitivity, can result in noisy or undersampled measurements. Further, depending on the resolution, sample size and data acquisition rates, the resulting noisy dataset can be in the order of terabytes. Advanced iterative reconstruction techniques can produce high-quality images from noisy measurements, but their computational requirements have made their use an exception rather than the rule. We propose a novel memory-centric approach that avoids redundant computations at the expense of additional memory complexity. We develop a memory-centric iterative reconstruction system, MemXCT, that uses an optimized SpMV implementation with two-level pseudo-Hilbert ordering and multi-stage input buffering. We evaluate MemXCT on various supercomputer architectures involving KNL and GPU. MemXCT can reconstruct a large (11Kx11K) mouse brain tomogram in 10 seconds using 4096 KNL nodes (256K cores). The results presented in our original article at the SC19 were based on large-scale supercomputing resources. The MemXCT application was selected for the Student Cluster Competition (SCC) Reproducibility Challenge and evaluated on a variety of cloud computing resources by universities around the world in the SC20 conference. We summarize the results of the top-ranked SCC Reproducibility Challenge teams and identify the most pertinent measures for ensuring the reproducibility of our experiments in this article.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Accelerating iterative ptychography with an integrated neural network

Electron ptychography is a powerful and versatile tool for high-resolution and dose-efficient imaging. Iterative reconstruction algorithms are powerful but also computationally expensive due to their relative complexity and the many hyperparameters that must be optimised. Gradient descent-based iterative ptychography is a popular method, but it may converge slowly when reconstructing low spatial frequencies. Here, in this work, we present a method for accelerating a gradient descent-based iterative reconstruction algorithm by training a neural network (NN) that is applied in the reconstruction loop. The NN works in Fourier space and selectively boosts low spatial frequencies, thus enabling faster convergence in a manner similar to accelerated gradient descent algorithms. We discuss the difficulties that arise when incorporating a NN into an iterative reconstruction algorithm and show how they can be overcome with iterative training. We apply our method to simulated and experimental data of gold nanoparticles on amorphous carbon and show that we can significantly speed up ptychographic reconstruction of the nanoparticles.

4DSTEM↗
DOE OSTI2020

TomoGAN: low-dose synchrotron x-ray tomography with generative adversarial networks: discussion

Synchrotron-based x-ray tomography is a noninvasive imaging technique that allows for reconstructing the internal structure of materials at high spatial resolutions from tens of micrometers to a few nanometers. In order to resolve sample features at smaller length scales, however, a higher radiation dose is required. Therefore, the limitation on the achievable resolution is set primarily by noise at these length scales. We present TomoGAN, a denoising technique based on generative adversarial networks, for improving the quality of reconstructed images for low-dose imaging conditions. We evaluate our approach in two photon-budget-limited experimental conditions: (1) sufficient number of low-dose projections (based on Nyquist sampling), and (2) insufficient or limited number of high-dose projections. In both cases, the angular sampling is assumed to be isotropic, and the photon budget throughout the experiment is fixed based on the maximum allowable radiation dose on the sample. Evaluation with both simulated and experimental datasets shows that our approach can significantly reduce noise in reconstructed images, improving the structural similarity score of simulation and experimental data from 0.18 to 0.9 and from 0.18 to 0.41, respectively. Furthermore, the quality of the reconstructed images with filtered back projection followed by our denoising approach exceeds that of reconstructions with the simultaneous iterative reconstruction technique, showing the computational superiority of our approach.

42 ENGINEERING↗
DOE OSTI2026

Associated Particle Imaging of Neutron Inelastic Scatter: 3-D Reconstruction, Capabilities, and Challenges

Associated particle imaging (API) offers unique advantages for 3-D imaging of neutron inelastic scatter, including single-view tomographic imaging and data acquisition when access is limited to only one side of the interrogated object. However, widespread adoption of neutron inelastic scatter imaging has been impeded by several inherent challenges, most prominently spatial resolution, self-attenuation, and statistical noise. Here, in this work, the capabilities and challenges of neutron inelastic scatter imaging are investigated. Instead of focusing on a single imaging application, we identify imaging principles that hold for various neutron inelastic scatter imaging techniques. The primary challenges for 3-D imaging are characterized. The inherent spatial resolution in the time-of-flight (TOF) dimension is derived based on the known system timing resolution and scan geometry. Three reconstruction algorithms are described and demonstrated, including the introduction of modern iterative reconstruction incorporating a physics-based system model. Simulation is leveraged to demonstrate imaging capability with varying coincidence count levels and system timing resolution. An example of measured data with both back-scatter and forward-scatter detector positioning is presented. System design characteristics and their effects on image quality are identified. The imaging framework presented in this article has the potential to facilitate growth of 3-D neutron inelastic scatter API by identifying applications that are a good match for the technique and by targeting system development resources toward the requirements of a specific imaging task.

Associated particle imaging (API)↗
DOE OSTI2023

An Edge Alignment-Based Orientation Selection Method for Neutron Tomography

Neutron computed tomography (nCT) is a 3D char-acterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences. A typical workflow involves placing the sample in the path of a neutron beam, acquiring projection data at a predefined set of orientations, and processing the resulting data using an analytic reconstruction algorithm. Typical nCT scans require hours to days to complete and are then processed using conventional filtered back-projection (FBP), which performs poorly with sparse views or noisy data. Hence, the main methods in order to reduce overall acquisition time are the use of an improved sampling strategy combined with the use of advanced reconstruction methods such as model-based iterative reconstruction (MBIR). In this paper, we propose an adaptive orientation selection method in which an MBIR reconstruction on previously-acquired measurements is used to define an objective function on orientations that balances a data-fitting term promoting edge alignment and a regularization term promoting orientation diversity. Using simulated and experimental data, we demonstrate that our method produces high-quality reconstructions using significantly fewer total measurements than the conventional approach.

Yang, Diyu↗
DOE OSTI2024

Dual X-ray computed tomography-aided classification of melt pool boundaries and flaws in crept additively manufactured parts

In metal additive manufacturing (AM), understanding the process-structure-performance relationships requires a combination of multi-scale characterization techniques that allows for the measurement of the melt pool shape and boundary and classifying various defects and flaws in the AM parts. Such approaches can be destructive, only 2D in nature, or have a small field of view and can be complex to co-register and analyze. Here, in this work, we present a non-destructive 3D inspection technique that employs dual-energy X-ray computed tomography (XCT) along with a model-based iterative reconstruction (MBIR) and a new segmentation algorithm. The proposed approach and algorithm are not only capable of classifying and quantifying flaws such as pores, cracks, and inclusions, but they also allow for the extraction of microstructural features such as melt pool boundaries (MPB) and melt pool regions (MPR), that can help understand process-structure-performance relationships for alloys under study. As an exemplar application, we employed the method for characterization of an additively manufactured aluminum alloy crept under tensile stress at 300 °C for 1064 h. Our results demonstrate high quality segmentation and classification of various flaws and MPB and MPR, for the first time, using 3D X-ray CT inspection. The delineated MPB and MPR in the crept samples reveal the preferential growth paths of cracks that formed during creep deformation. The technique was used for successfully quantifying the characteristics (number of defects, their density, volume fraction, etc.) of the manufacturing-induced pores and creep-induced cracks, which is necessary to better understand the creep failure mechanisms of the material.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Hydrodynamic and Radiographic Toolbox (HART)

With a multi-lab and university team, we propose to develop new methods for a Hydrodynamic and Radiographic Toolbox (HART) that will enable a fuller and more extensive use of experimental radiographic data towards better characterizing and reducing uncertainties in predictive modeling of weapons performance. We will achieve this by leveraging recent developments in areas of computational imaging, statistical and machine learning, and reduced order modeling of hydrodynamics. In terms of software practices, by partnering with XCP (RISTRA Project) we will conform to recent XCP standards that are inline with modern software practices and standards and ensure compatibility and ease of inter-operability with existing codes. Three new activities under this proposal include (a) the development and use of deep learning-based surrogates to accelerate reconstruction and variational inference of density fields from radiographs of hydrotests, (b) a model-data fusion strategy that couples deep learning-based density reconstructions with fast hydrodynamics simulators to better constrain the reconstruction, and (c) a method for treating asymmetries using techniques adopted from limited view tomography. All three new activities will be based on improved treatment of scatter, noise, beam spot movement, detector blur, and flat fielding in the forward model, and a use of sophisticated priors to aid in the re construction. The improvements to the forward model and improved algorithmic design of the reconstruction when complete will be contained in the iterative reconstruction code SHIVA—a code project that we have recently initiated. The many ways in which machine learning can be used in the reconstruction work will be contained in a code HERMES that has been initiated with DTRA support. For example, the significant levels of acceleration that will likely be achieved by the use of machine learning techniques will permit us (and are required) to quantify uncertainties in density retrievals. Next, the two-way coupling between density reconstruction and model-based simulation of the hydrodynamics will be contained in code EREBUS, and will permit a fuller realization of the potential of the data to constrain the hydrodynamic model and better address issues related to asymmetries in the problem. Finally, we anticipate that the better consistency with physics achieved in our reconstructions will allow them to be used by X-Division more so than today.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Combining Deep Learning and scatterControl for High-Throughput X-ray CT Based Non-Destructive Characterization of Large-Scale Casted Metallic Components

X-ray computed tomography (XCT) is essential for nondestructive evaluation and quality control of large-scale metal components. XCT imaging, however, faces significant challenges from metal artifacts, particularly those caused by Compton scattering, which degrade image quality and obscure critical details. Hardware-based solutions (e.g. scatterControl) offer advancements by intercepting scattered photons and reducing artifacts, but they can be time-consuming and require additional processing. Here, we propose modifying and leveraging a novel deep learning (DL) framework, Simurgh, to enhance and accelerate scatter correction in XCT. By combining scatterControl with DL-based artifact removal, we demonstrate significant reduction in scan time while producing high-quality reconstructions. Through extensive evaluation on industrial XCT data, we show that our methods reduce scan time by up to more than 10 x while preserving flaw detectability. Quantitative analysis across multiple segmentation techniques confirms that Simurgh-based reconstructions consistently outperform traditional Feldkamp-Davis-Kress, model-based iterative reconstruction, and commercial DL models in both pixel-level and task-specific evaluations, enabling scalable, high-throughput XCT workflows for characterization of large scale components in applications such as casting and metal additive manufacturing.

Complex metal parts↗
DOE OSTI2020

Fast Scanning Probe Microscopy via Machine Learning: Non-Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functional imaging are few in number. Here, using piezoresponse force microscopy (PFM) as a model application, a factor of 5.8 reduction in data collection using a combination of sparse spiral scanning with compressive sensing and Gaussian process regression reconstruction is demonstrated. It is found that even extremely sparse spiral scans offer strong reconstructions with less than 6% error for Gaussian process regression reconstructions. Further, the error associated with each reconstructive technique per reconstruction iteration is analyzed, finding the error is similar past ≈15 iterations, while at initial iterations Gaussian process regression outperforms compressive sensing. Finally, this study highlights the capabilities of reconstruction techniques when applied to sparse data, particularly sparse spiral PFM scans, with broad applications in scanning probe and electron microscopies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Model-Based Reconstruction for Multi-Frequency Collimated Beam Ultrasound Systems

Collimated beam ultrasound systems are a technology for imaging inside multi-layered structures such as geothermal wells. These systems work by using a collimated narrow-band ultrasound transmitter that can penetrate through multiple layers of heterogeneous material. A series of measurements can then be made at multiple transmit frequencies. However, commonly used reconstruction algorithms such as Synthetic Aperture Focusing Technique (SAFT) tend to produce poor quality reconstructions for these systems both because they do not model collimated beam systems and they do not jointly reconstruct the multiple frequencies. Here, in this article, we propose a multi-frequency ultrasound model-based iterative reconstruction (UMBIR) algorithm designed for multi-frequency collimated beam ultrasound systems. The combined system targets reflective imaging of heterogeneous, multi-layered structures. For each transmitted frequency band, we introduce a physics-based forward model to accurately account for the propagation of the collimated narrow-band ultrasonic beam through the multi-layered media. We then show how the joint multi-frequency UMBIR reconstruction can be computed by modeling the direct arrival signals, detector noise, and incorporating a spatially varying image prior. Results using both simulated and experimental data indicate that multi-frequency UMBIR reconstruction yields much higher reconstruction quality than either single frequency UMBIR or SAFT.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time

Abstract Noninvasive X-ray imaging of nanoscale three-dimensional objects, such as integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of the complex electromagnetic field through the IC; combined with a tomographic scan, which collects these complex field projections from multiple angles. Here, we present Attentional Ptycho-Tomography (APT), an approach to drastically reduce the amount of angular scanning, and thus the total acquisition time. APT is machine learning-based, utilizing axial self-Attention for Ptycho-Tomographic reconstruction. APT is trained to obtain accurate reconstructions of the ICs, despite the incompleteness of the measurements. The training process includes regularizing priors in the form of typical patterns found in IC interiors, and the physics of X-ray propagation through the IC. We show that APT with ×12 reduced angles achieves fidelity comparable to the gold standard Simultaneous Algebraic Reconstruction Technique (SART) with the original set of angles. When using the same set of reduced angles, then APT also outperforms Filtered Back Projection (FBP), Simultaneous Iterative Reconstruction Technique (SIRT) and SART. The time needed to compute the reconstruction is also reduced, because the trained neural network is a forward operation, unlike the iterative nature of these alternatives. Our experiments show that, without loss in quality, for a 4.48 × 93.2 × 3.92 µm 3 IC (≃6 × 10 8 voxels), APT reduces the total data acquisition and computation time from 67.96 h to 38 min. We expect our physics-assisted and attention-utilizing machine learning framework to be applicable to other branches of nanoscale imaging, including materials science and biological imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Evaluation of the Reconstruction Accuracy of the Ultrasound Model–Based Image Reconstruction (U-MBIR) Method for Concrete to Optimize Damage Detection

Reinforced concrete is a critical structural material used to construct nuclear power plants (NPPs). As such, its safety and performance must be thoroughly examined throughout the life cycle of the NPP infrastructure system. Ultrasonic measurements have been an industry standard for both surface and subsurface inspections. To support the development of these techniques, Oak Ridge National Laboratory (ORNL) has researched and developed advanced image reconstruction algorithms to capture internal damage. The results and discussion presented herein summarize the current state of the ultrasonic model–based iterative reconstruction (U-MBIR) algorithm developed at ORNL. In the work documented in this report, the U-MBIR methodology was applied to four sets of ultrasonic data collected from concrete specimens. The results demonstrate that the U-MBIR algorithm can successfully detect defects within the four concrete samples. The reconstruction images help identify the specimen thickness, regions of delamination, and location of rebar embedded within the concrete. The reconstruction images allow engineers and technicians to characterize the internal defects within concrete specimens and structural members. Ultimately, this knowledge can guide engineers in making informed decisions regarding the performance, safety, and reliability of structural materials (i.e., reinforced concrete) throughout the life cycle of NPPs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Complete Performance Comparison Between the Optimized Image Construction Algorithm (U-MBIR) and the Existing Reconstruction Algorithm for Detecting Defects and Damage in Concrete

Reinforced concrete (RC) is a composite material subjected to mechanical, thermal, and chemical loads throughout its service life. Because of these external stressors and the susceptibility of RC structural members to shrinkage and microcracking, the material degrades throughout its life cycle. This deterioration can lead to a decrease in member capacity and, ultimately, poses a threat to the structural integrity. Thus, it is crucial that the damage caused by aging and degradation be monitored and assessed at regular intervals throughout the material’s service life. Since coring of the material is typically not feasible for in-service structural systems, non-destructive evaluation (NDE) methodologies are used to assess remaining structural capacity. NDE methods enable surface and subsurface examination without damaging or degrading the medium. Moreover, RC is a critical component of nuclear power plants; thus, its safety and reliability must be thoroughly examined throughout the life cycle of the structural system. Ultrasonic measurements have been an industry standard for both surface and subsurface inspections. To this end, Oak Ridge National Laboratory (ORNL) has researched and developed advanced image reconstruction algorithms to capture internal damage. The results and discussion presented herein summarize the current state of the ultrasonic model–based iterative reconstruction (U-MBIR) algorithm developed at ORNL. More specifically, this report presents a comparison between reconstruction images produced via a widely employed ultrasonic NDE technique—the synthetic aperture focusing technique (SAFT)—and the ORNL-developed U-MBIR algorithm. These NDE methodologies are demonstrated using ultrasonic data collected from four concrete specimens. Overall, the U-MBIR algorithm eliminates artifacts and noise that are typically present within the SAFT reconstructions, and it shows defects and anomalies more clearly than the SAFT images. In conclusion, this algorithm is suitable for identifying concrete defects, although more improvements and optimization could be done to better define internal defects.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Reconstructing cosmic polarization rotation with ResUNet-CMB

Cosmic polarization rotation, which may result from parity-violating new physics or the presence of primordial magnetic fields, converts E-mode polarization of the cosmic microwave background (CMB) into B-mode polarization. Anisotropic cosmic polarization rotation leads to statistical anisotropy in CMB polarization and can be reconstructed with quadratic estimator techniques similar to those designed for gravitational lensing of the CMB. At the sensitivity of upcoming CMB surveys, lensing-induced B-mode polarization will act as a limiting factor in the search for anisotropic cosmic polarization rotation, meaning that an analysis which incorporates some form of delensing will be required to improve constraints on the effect with future surveys. In this study we extend the ResUNet-CMB convolutional neural network to reconstruct anisotropic cosmic polarization rotation in the presence of gravitational lensing and patchy reionization, and we show that the network simultaneously reconstructs all three effects with variance that is lower than that from the standard quadratic estimator nearly matching the performance of an iterative reconstruction method.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Progress on Associated-Particle Imaging Algorithms, 2022

The present work describes progress on developing imaging algorithms that use fast neutron signatures acquired using the associated-particle imaging (API) method. The present work complements ongoing work to develop neutron source and detector hardware to enable field inspection by investigating algorithms that are capable of discriminating among critical materials or extracting three-dimensional (3D) geometrical information from single-sided or transmission measurements. The present work is divided into three approaches: 1.Iterative reconstruction of inelastic gamma-ray emissions to perform 3D time-of-flight (TOF) imaging in a single view in either transmission or backscatter configurations. Iterative reconstruction enables image resolution better than the inherent TOF resolution. 2.Decomposition of registered neutron and x-ray radiographs into an assumed material list for each pixel in the image. 3.Material identification using full spectral analysis that includes the emergent neutron and gamma ray energies, times, and angles. Progress for each approach is summarized for fiscal year (FY) 2022.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Initial Refinement and Optimization of the Image Reconstruction (U-MBIR) Algorithm to Optimize its Performance in Detecting Damage and Flaws

Concrete is a critical component of nuclear power plants; thus, its safety and reliability must be thoroughly examined throughout the life cycle of the structural system. During the life cycle of this infrastructure, monitoring of the concrete for signs of degradation should be performed through nondestructive evaluation (NDE). Ultrasonic measurements have been an industry standard for both surface and subsurface inspections. As such, Oak Ridge National Laboratory (ORNL) is developing advanced image reconstruction algorithms to overcome the limitations of traditional ultrasonic NDE methodologies. The results and discussion presented herein summarize the current state of the ultrasonic model–based iterative reconstruction (U-MBIR) algorithm developed at ORNL.

36 MATERIALS SCIENCE↗
DOE OSTI2024

X-ray nano-holotomography reconstruction with simultaneous probe retrieval

In conventional tomographic reconstruction, the pre-processing step includes flat-field correction, where each sample projection on the detector is divided by a reference image taken without the sample. When using coherent X-rays as a probe, this approach overlooks the phase component of the illumination field (probe), leading to artifacts in phase-retrieved projection images, which are then propagated to the reconstructed 3D sample representation. The problem intensifies in nano-holotomography with focusing optics, which, due to various imperfections creates high-frequency components in the probe function. Here, we present a new iterative reconstruction scheme for holotomography, simultaneously retrieving the complex-valued probe function. Implemented on GPUs, this algorithm results in 3D reconstruction resolving twice thinner layers in a 3D ALD standard sample measured using nano-holotomography.

Nikitin, Viktor↗